/aosp_15_r20/external/pytorch/test/torch_np/numpy_tests/lib/ |
H A D | test_type_check.py | 27 nan_to_num, 41 nan_to_num, 359 vals = nan_to_num(np.array((-1.0, 0, 1)) / 0.0) 366 vals = nan_to_num(np.array((-1.0, 0, 1)) / 0.0, nan=10, posinf=20, neginf=30) 372 vals = nan_to_num([1]) 375 vals = nan_to_num([1], nan=10, posinf=20, neginf=30) 381 vals = nan_to_num(1) 384 vals = nan_to_num(1, nan=10, posinf=20, neginf=30) 390 vals = nan_to_num(1.0) 393 vals = nan_to_num(1.1, nan=10, posinf=20, neginf=30) [all …]
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/aosp_15_r20/external/pytorch/benchmarks/operator_benchmark/pt/ |
H A D | nan_to_num_test.py | 8 """Microbenchmarks for torch.nan_to_num / nan_to_num_ operators""" 10 # Configs for PT torch.nan_to_num / nan_to_num_ operators 15 ["nan_to_num", torch.nan_to_num], 44 self.set_module_name("nan_to_num")
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/aosp_15_r20/external/pytorch/benchmarks/static_runtime/ |
H A D | test_static_module.cc | 381 c = (a + b).relu().nan_to_num().float() in TEST() 382 d = a.flatten().nan_to_num() * b.flatten().nan_to_num() in TEST() 392 c = (a + b).relu().nan_to_num().float() in TEST() 393 d = a.flatten().nan_to_num() * b.flatten().nan_to_num() in TEST() 403 c = (a + b).relu().nan_to_num().float() in TEST() 405 e = a.flatten().nan_to_num() * b.flatten().nan_to_num() in TEST() 415 c = (a + b).relu().nan_to_num().float() in TEST() 417 e = a.flatten().nan_to_num() * b.flatten().nan_to_num() in TEST() 1728 z = y.nan_to_num() in TEST() 1736 EXPECT_FALSE(hasNodeWithKind(graph, "aten::nan_to_num")); in TEST() [all …]
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/aosp_15_r20/external/pytorch/test/ |
H A D | test_unary_ufuncs.py | 473 lambda x: x.nan_to_num(nan=nan, posinf=posinf), 474 lambda x: np.nan_to_num(x, nan=nan, posinf=posinf), 478 lambda x: x.nan_to_num(posinf=posinf, neginf=neginf), 479 lambda x: np.nan_to_num(x, posinf=posinf, neginf=neginf), 485 result = torch.nan_to_num(x) 486 torch.nan_to_num(x, out=out) 489 result = torch.nan_to_num(x, nan=nan, posinf=posinf, neginf=neginf) 490 torch.nan_to_num(x, out=out, nan=nan, posinf=posinf, neginf=neginf) 508 return torch.nan_to_num 527 res = torch.nan_to_num(a, **{kwarg_name: 12}) [all …]
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H A D | test_tensorexpr_pybind.py | 335 return a.nan_to_num() 343 …%y : Float(2, 2, strides=[2, 1], requires_grad=0, device=cpu) = aten::nan_to_num(%x, %none, %none,… 357 kernel = te.TensorExprKernel(graph, {"aten::nan_to_num": my_custom_lowering})
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/aosp_15_r20/external/pytorch/test/typing/pass/ |
H A D | math_ops.py | 252 # nan_to_num 254 torch.nan_to_num(x) 255 torch.nan_to_num(x, nan=2.0) 256 torch.nan_to_num(x, nan=2.0, posinf=1.0)
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/aosp_15_r20/external/pytorch/functorch/dim/ |
H A D | op_properties.py | 234 torch.Tensor.nan_to_num, 235 torch.nan_to_num,
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/aosp_15_r20/external/pytorch/test/mobile/model_test/ |
H A D | math_ops.py | 162 torch.nan_to_num(w), 164 torch.nan_to_num(w, nan=2.0), 165 torch.nan_to_num(w, nan=2.0, posinf=1.0),
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H A D | coverage.yaml | 423 - aten::nan_to_num 902 aten::nan_to_num: 3
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/aosp_15_r20/external/pytorch/torch/ao/quantization/fx/_model_report/ |
H A D | model_report_observer.py | 212 quantile_ratios = torch.nan_to_num(quantile_ratios) 239 new_ratios = torch.nan_to_num(new_ratios)
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/aosp_15_r20/external/pytorch/aten/src/ATen/native/sparse/ |
H A D | SparseUnaryOps.cpp | 45 #include <ATen/ops/nan_to_num.h> 266 return at::nan_to_num(t, nan, posinf, neginf); in nan_to_num_sparse()
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/aosp_15_r20/external/pytorch/torch/_numpy/ |
H A D | _funcs_impl.py | 858 def nan_to_num( function 861 # work around RuntimeError: "nan_to_num" not implemented for 'ComplexDouble' 863 re = torch.nan_to_num(x.real, nan=nan, posinf=posinf, neginf=neginf) 864 im = torch.nan_to_num(x.imag, nan=nan, posinf=posinf, neginf=neginf) 867 return torch.nan_to_num(x, nan=nan, posinf=posinf, neginf=neginf)
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/aosp_15_r20/external/pytorch/torch/onnx/ |
H A D | symbolic_opset10.py | 44 "nan_to_num", 750 @_onnx_symbolic("aten::nan_to_num") 752 def nan_to_num(g: jit_utils.GraphContext, input, nan, posinf, neginf): function
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/aosp_15_r20/external/pytorch/torch/distributed/tensor/_ops/ |
H A D | _pointwise_ops.py | 309 aten.nan_to_num.default, 310 aten.nan_to_num.out,
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/aosp_15_r20/external/pytorch/aten/src/ATen/native/mps/operations/ |
H A D | TensorCompare.mm | 454 "nan_to_num: dtype of out: ", 477 string key = "nan_to_num" + getTensorsStringKey({self});
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/aosp_15_r20/external/pytorch/aten/src/ATen/native/ |
H A D | UnaryOps.cpp | 97 #include <ATen/ops/nan_to_num.h> 810 "nan_to_num: dtype of out: ", in nan_to_num_out() 826 Tensor nan_to_num( in nan_to_num() function
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/aosp_15_r20/external/pytorch/torch/masked/maskedtensor/ |
H A D | unary.py | 54 "nan_to_num",
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/aosp_15_r20/external/pytorch/functorch/op_analysis/ |
H A D | public_api | 100 nan_to_num
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H A D | annotated_ops | 149 nan_to_num, composite pointwise
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/aosp_15_r20/external/pytorch/aten/src/ATen/functorch/ |
H A D | BatchRulesUnaryOps.cpp | 113 UNARY_POINTWISE_ALL(nan_to_num); in TORCH_LIBRARY_IMPL()
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/aosp_15_r20/external/pytorch/docs/source/ |
H A D | masked.rst | 121 nan_to_num
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H A D | torch.rst | 388 nan_to_num
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/aosp_15_r20/external/pytorch/torch/csrc/jit/runtime/static/ |
H A D | passes.cpp | 48 %y3 = aten::nan_to_num(%y2, %f, %g, %h) in ConcatAddMulReplaceNaNClip() 72 %y3 = aten::nan_to_num(%y2, %f, %g, %h) in ConcatAddMulReplaceNaNClip() 1400 %y : Tensor = aten::nan_to_num(%x, %nan, %posinf, %neginf) in FuseClampNaNToNum()
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/aosp_15_r20/external/pytorch/aten/src/ATen/native/cuda/ |
H A D | UnaryOpsKernel.cu | 228 AT_DISPATCH_COMPLEX_TYPES(iter.dtype(), "nan_to_num", [&]() { in nan_to_num_kernel_cuda()
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/aosp_15_r20/external/pytorch/torch/_decomp/ |
H A D | __init__.py | 370 aten.nan_to_num,
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